





'''simple docstring'''




from __future__ import annotations

import pandas as pd






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	list[int]:
     __A		:					Tuple						=			[0] * no_of_processes
     __A		:					str						=			[0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(__snake_case     ):
          __A		:					int						=			burst_time[i]

     __A		:					str						=			0
     __A		:					Tuple						=			0
     __A		:					Optional[Any]						=			9_99_99_99_99
     __A		:					int						=			0
     __A		:					Optional[Any]						=			False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(__snake_case     ):
               if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                    if remaining_time[j] < minm:
                         __A		:					Dict						=			remaining_time[j]
                         __A		:					Optional[int]						=			j
                         __A		:					Union[str, Any]						=			True

          if not check:
               increment_time += 1
               continue
          remaining_time[short] -= 1

          __A		:					str						=			remaining_time[short]
          if minm == 0:
               __A		:					Any						=			9_99_99_99_99

          if remaining_time[short] == 0:
               complete += 1
               __A		:					List[Any]						=			False

               # Find finish time of current process
               __A		:					Any						=			increment_time + 1

               # Calculate waiting time
               __A		:					Optional[int]						=			finish_time - arrival_time[short]
               __A		:					Dict						=			finar - burst_time[short]

               if waiting_time[short] < 0:
                    __A		:					List[str]						=			0

        # Increment time
          increment_time += 1
     return waiting_time






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					int					,				__snake_case    :					list[int]     )	->	list[int]:
     __A		:					Optional[Any]						=			[0] * no_of_processes
     for i in range(__snake_case     ):
          __A		:					Any						=			burst_time[i] + waiting_time[i]
     return turn_around_time






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	None:
     __A		:					Union[str, Any]						=			0
     __A		:					Optional[int]						=			0
     for i in range(__snake_case     ):
          __A		:					Optional[Any]						=			total_waiting_time + waiting_time[i]
          __A		:					List[str]						=			total_turn_around_time + turn_around_time[i]
     print(f'Average waiting time = {total_waiting_time / no_of_processes:.5f}'     )
     print('Average turn around time ='					,				total_turn_around_time / no_of_processes     )


if __name__ == "__main__":
  print('''Enter how many process you want to analyze''')
  lowercase__       : Dict         =       int(input())
  lowercase__       : str         =       [0] * no_of_processes
  lowercase__       : int         =       [0] * no_of_processes
  lowercase__       : Tuple         =       list(range(1, no_of_processes + 1))

  for i in range(no_of_processes):
    print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
    lowercase__      ,				lowercase__       : Dict         =       map(int, input().split())

  lowercase__       : int         =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)

  lowercase__       : Any         =       burst_time
  lowercase__       : Tuple         =       no_of_processes
  lowercase__       : List[str]         =       waiting_time
  lowercase__       : List[Any]         =       calculate_turnaroundtime(bt, n, wt)

  calculate_average_times(waiting_time, turn_around_time, no_of_processes)

  lowercase__       : str         =       pd.DataFrame(
      list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
      columns=[
          '''Process''',
          '''BurstTime''',
          '''ArrivalTime''',
          '''WaitingTime''',
          '''TurnAroundTime''',
      ],
  )

  # Printing the dataFrame
  pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
  print(fcfs)







'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint


@require_torch
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[str]:
								# For consistency across different places the DisjunctiveConstraint is called,
								# dc.token_ids is a list of integers. It is also initialized only by integers.

								UpperCAmelCase_      :       Union[str, Any]          =       [[1, 2, 4], [1, 2, 3, 4]]
								UpperCAmelCase_      :       Dict          =       DisjunctiveConstraint(_A      )
								self.assertTrue(isinstance(dc.token_ids				, _A      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]      )      )

								with self.assertRaises(_A      ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]      ), torch.LongTensor([1, 2, 3, 4, 5]      )]      )




					def 		A      (  self  :		Optional[int]      )    ->							List[Any]:
								# We can't have constraints that are complete subsets of another. This leads to a preverse
								# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
								# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
								# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
								# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
								UpperCAmelCase_      :       Optional[Any]          =       [[1, 2], [1, 2, 3, 4]]

								with self.assertRaises(_A      ):
											DisjunctiveConstraint(_A      )  # fails here




					def 		A      (  self  :		List[str]      )    ->							int:
								UpperCAmelCase_      :       Dict          =       [[1, 2, 3], [1, 2, 4]]

								UpperCAmelCase_      :       List[Any]          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								UpperCAmelCase_      :       int          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(2      )
								UpperCAmelCase_      :       Optional[int]          =       stepped is True and completed is False and reset is False
								self.assertTrue(_A      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(3      )
								UpperCAmelCase_      :       List[Any]          =       stepped is True and completed is True and reset is False
								self.assertTrue(_A      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 3]      )





					def 		A      (  self  :		List[Any]      )    ->							Any:
								UpperCAmelCase_      :       int          =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								UpperCAmelCase_      :       str          =       DisjunctiveConstraint(_A      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       dc.update(4      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.current_seq == [1, 2, 4]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.current_seq == [1, 2, 4, 5]      )

								dc.reset()

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       dc.update(1      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 3      )
								self.assertTrue(dc.current_seq == [1]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       dc.update(2      )
								self.assertTrue(not dc.completed      )
								self.assertTrue(dc.remaining() == 2      )
								self.assertTrue(dc.current_seq == [1, 2]      )

								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       dc.update(5      )
								self.assertTrue(dc.completed      )  # Completed!
								self.assertTrue(dc.remaining() == 0      )
								self.assertTrue(dc.current_seq == [1, 2, 5]      )


